Custom AI
Custom AI systems for Australian B2B teams
A full system - document management and multi-agent orchestration - defined and scoped like a software project, built on your compute, with process mapping as the foundation.
What we build
Document management and a team of agents.
Not a chatbot bolted on the side. A private AI system that manages your documents and runs a team of agents over them, scoped and delivered like a software development project.
Document management
Ingest, organise and version the document pile - leases, statements, contracts - into a structured, searchable foundation the agents work from.
Multi-agent orchestration
Named agents - document, research, credit, drafting, CRM, comms - that each own a job and hand off to each other under your control.
Extraction & structuring
Pulling the fields that matter out of messy, complex documents reliably, so downstream steps run on clean data.
Private deployment
Run inside your perimeter, on your compute, with the model choice that fits. Your client data never leaves your control.
Security & governance
Permissions, access control and a full audit trail - in regulated finance, non-negotiable - baked in from the start.
Integration
Wired into the systems people already use - CRM, email, document store - so it becomes part of the work, not a side tool nobody opens.
Why Vanillah
Why most AI pilots never become core process.
Scoped like a software project
Defined deliverables, phases and a plan - not an open-ended experiment. You know what's being built, and what good looks like.
Measured, not assumed
We define the job and the success metric up front, then validate against real past cases before anything goes live.
Private and on your compute
On-prem or in your cloud, your data never leaving your control - the only way document-heavy finance work can use AI.
Built for adoption
Validation, integration and change management are part of the build - the steps that turn a demo into a process people trust daily.
How we build it
A twelve-step lifecycle, in three layers.
Most firms can do the build steps if they push hard enough. They stall because they never did the foundation (a solution looking for a problem) or never reached the trust layer (it works in a demo, but nobody validated, integrated or adopted it). Embedded in a core process is a trust-layer achievement.
Foundation layer
Before any build. You can't enhance a process you haven't drawn.
Process mapping (as-is)
Map how the work actually runs today, including the handoffs and decision points. This leads, it doesn't sit sixth.
Outcome & success metric
What job, what good looks like, and how you'll measure the return. Measured, not assumed, is literally a step.
Build layer
The part most firms can reach if they push hard enough.
Data & document quality
What exists, what format, how messy - the raw material the whole system depends on.
Extraction & structuring
Pulling the important fields out of complex documents reliably. Its own step, distinct from data quality.
Infrastructure
Hardware, software, model choice and private deployment inside your perimeter.
Security, permissions & governance
Access control plus the audit trail, which in finance is non-negotiable.
RAG configuration
Retrieval, grounding and citations, so answers are traceable to the source document.
AI-enhanced process design
The process map redrawn with human-in-the-loop checkpoints, where AI drafts and a person approves.
Trust layer
The part most pilots skip, and the reason only a fraction reach core process.
Validation & evaluation
Does it produce reliable output, tested against real past cases, with hallucination and edge-case checks. In regulated finance you cannot embed without this.
Integration
Wiring it into the systems people already use - CRM, email, document store - or it stays a side tool nobody opens.
Adoption & change management
Getting people to trust it and use it daily. The single biggest reason pilots never become core processes.
Monitoring & iteration
Tuning for drift and new document types over time. Run and sharpen.
Custom AI, answered.
What do you mean by a custom AI system?
A private system that manages your documents and runs a team of agents over them - document management plus multi-agent orchestration - scoped and delivered like a software development project, not an off-the-shelf chatbot. It's built around your specific process and runs on your compute.
Why scope it like a software project?
Because that's what makes it real. Defined deliverables, phases and success metrics mean you know what's being built and how you'll measure the return - rather than an open-ended experiment that demos well and then stalls.
Why does process mapping come first?
You can't enhance a process you haven't drawn. Mapping the as-is workflow - handoffs, decision points, exceptions - is the foundation. Firms that skip it end up with a solution looking for a problem.
Is our data safe?
Yes. The system runs inside your perimeter, on your compute or cloud, with permissions, access control and a full audit trail. Your client data never leaves your control - which in regulated finance is the only way AI on sensitive documents is viable.
Why do so many AI pilots fail to become core process?
They stall in one of two places: the foundation (they built a solution looking for a problem) or the trust layer (it worked in a demo but nobody validated, integrated or adopted it). Being embedded in a core process is a trust-layer achievement, which is exactly the part we build in.
Do you only work in finance and property?
That's our deepest expertise, but we've also worked with many other industries. An adjusted lifecycle can be applied to any business where we believe we can add value to get AI built into the core process.
Let's scope your AI system.
Book a call and we'll map a core process with you and show you what a private, validated AI system would look like running on it.